Automation has greatly increased the efficiency of retail checkout operations over the years. Employee operated checkout operations have gained greatly in efficiency, and self checkout operations have also become possible, providing for significant labor savings for merchants and increased convenience for customers choosing to use self checkout. One problem associated with checkout operations is the need to prevent theft. This concern is also applicable in employee operated systems, but is of particular importance in the context of customer operated self checkout. If a transaction is not closely monitored, numerous opportunities exist for an unscrupulous customer to take merchandise without entering it into a transaction, but using an employee to guard against such theft adds labor costs that might be avoided if monitoring of purchases can be further automated and improved.
Several prior art systems use weight scales to match items against transaction entries, for example by actual versus expected weight comparisons. Systems may also use imaging or other optical techniques to compare an approximate volume of an item against an expected volume. Image capture has been used to store images for review or to provide images to a monitoring station, but specific automated matching of item images against transaction entries has not heretofore been successfully performed in the manner to be described herein. One obstacle to automated image matching is that a comprehensive comparison of an item against the large number of items, such as the 40,000 to 100,000 items that might be carried by a large retailer, could require more time than would be acceptable for a customer or for a merchant.